4 research outputs found
An Information Theoretic Location Verification System for Wireless Networks
As location-based applications become ubiquitous in emerging wireless
networks, Location Verification Systems (LVS) are of growing importance. In
this paper we propose, for the first time, a rigorous information-theoretic
framework for an LVS. The theoretical framework we develop illustrates how the
threshold used in the detection of a spoofed location can be optimized in terms
of the mutual information between the input and output data of the LVS. In
order to verify the legitimacy of our analytical framework we have carried out
detailed numerical simulations. Our simulations mimic the practical scenario
where a system deployed using our framework must make a binary Yes/No
"malicious decision" to each snapshot of the signal strength values obtained by
base stations. The comparison between simulation and analysis shows excellent
agreement. Our optimized LVS framework provides a defence against location
spoofing attacks in emerging wireless networks such as those envisioned for
Intelligent Transport Systems, where verification of location information is of
paramount importance
Optimal Information-Theoretic Wireless Location Verification
We develop a new Location Verification System (LVS) focussed on network-based
Intelligent Transport Systems and vehicular ad hoc networks. The algorithm we
develop is based on an information-theoretic framework which uses the received
signal strength (RSS) from a network of base-stations and the claimed position.
Based on this information we derive the optimal decision regarding the
verification of the user's location. Our algorithm is optimal in the sense of
maximizing the mutual information between its input and output data. Our
approach is based on the practical scenario in which a non-colluding malicious
user some distance from a highway optimally boosts his transmit power in an
attempt to fool the LVS that he is on the highway. We develop a practical
threat model for this attack scenario, and investigate in detail the
performance of the LVS in terms of its input/output mutual information. We show
how our LVS decision rule can be implemented straightforwardly with a
performance that delivers near-optimality under realistic threat conditions,
with information-theoretic optimality approached as the malicious user moves
further from the highway. The practical advantages our new
information-theoretic scheme delivers relative to more traditional Bayesian
verification frameworks are discussed.Comment: Corrected typos and introduced new threat model
Wireless Location Verification and Acquisition Using Machine Learning
Traditional wireless location verification (authentication) is only feasible under the assumption that radio propagation is described by simple time-independent mathematical models. A similar situation applies to location acquisition, albeit to a lesser extent. However, in real-world situations, channel conditions are rarely well-described by simple mathematical models. In this thesis, novel location verification and acquisition techniques that integrate machine learning algorithms into the decision process are designed, analysed, and tested. Through the use of both simulated and experimental data, it is shown how the novel solutions developed remain operational in unknown time-varying channel conditions, thus making them superior to existing solutions, and more importantly, deployable in real-world scenarios. Location verification will be of growing importance for a host of emerging wireless applications in which location information plays a pivotal role. The location verification solutions offered in this thesis are the first to be tested against experimental data and the first to invoke machine learning algorithms. As such, they likely form the foundation for all future verification algorithms